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library(caret)

## Loading required package: lattice

## Loading required package: ggplot2

library(data.table)
library(plotly)

## 
## Attaching package: 'plotly'

## The following object is masked from 'package:ggplot2':
## 
##     last_plot

## The following object is masked from 'package:stats':
## 
##     filter

## The following object is masked from 'package:graphics':
## 
##     layout

# attach iris
data(iris)

dt <- iris

# obtain 80% of the data set for training
validation_index <- createDataPartition(dt$Species, p = 0.8, list = FALSE)
# select 20% for validation and 80% for training
dtValidation <- dt[-validation_index, ] %>% data.table()
dtTest <- dt[validation_index, ] %>% data.table()

Summarize data:

dimensions
types of attributes
peak at the data
levels of classes,
breakdown of the instances in each class
statistical summary of all attributes

# dimensions
dim(dtTest)

## [1] 120   5

# list attributes
sapply(dt, class)

## Sepal.Length  Sepal.Width Petal.Length  Petal.Width      Species 
##    "numeric"    "numeric"    "numeric"    "numeric"     "factor"

# peek at dataset
head(dt)

# list the levels for the class
levels(dt$Species)

## [1] "setosa"     "versicolor" "virginica"

# class distribution of test data
percentage <- prop.table(table(dtTest$Species))*100
cbind(freq = table(dtTest$Species), percentage = percentage)

##            freq percentage
## setosa       40   33.33333
## versicolor   40   33.33333
## virginica    40   33.33333

# statistical summary
summary(dtTest)

##   Sepal.Length    Sepal.Width     Petal.Length    Petal.Width   
##  Min.   :4.300   Min.   :2.000   Min.   :1.000   Min.   :0.100  
##  1st Qu.:5.100   1st Qu.:2.800   1st Qu.:1.600   1st Qu.:0.300  
##  Median :5.700   Median :3.000   Median :4.250   Median :1.300  
##  Mean   :5.829   Mean   :3.026   Mean   :3.758   Mean   :1.192  
##  3rd Qu.:6.400   3rd Qu.:3.300   3rd Qu.:5.100   3rd Qu.:1.800  
##  Max.   :7.900   Max.   :4.100   Max.   :6.900   Max.   :2.500  
##        Species  
##  setosa    :40  
##  versicolor:40  
##  virginica :40  
##                 
##                 
##

Visualize Dataset

# univariate plots: boxplot of each individual variable
dtPlot <- melt.data.table(dtTest, id.vars = "Species",variable.name = "Type", value.name = "Value", 
                          measure.vars = c("Sepal.Length", "Sepal.Width", "Petal.Length", "Petal.Width"))
dtPlot$Normalized <- (dtPlot$Value - min(dtPlot$Value))/(max(dtPlot$Value) - min(dtPlot$Value))
plot_ly(dtPlot, y = ~Normalized, color = ~Type, type = "box", boxpoints = "all", jitter = 0.3)

# bar plot of class breakdown
plot_ly(dtTest[, .N, keyby = Species], y = ~N, x = ~Species, type = "bar")

# multivariate plots: feature plot of attributes and color by class, box plot for each attribute by species

Evaluate Some Algorithms

Set-up the test harness to use 10-fold cross validation

This will split our dataset into 10 parts, train in 9 and test on 1 and release for all combinations of train-test splits. We will also repeat the process 3 times for each algorithm with different splits of the data into 10 groups, in an effort to get a more accurate estimate.
the metric of “Accuracy” to evaluate models.
- This is a ratio of the number of correctly predicted instances in divided by the total number of instances in the dataset multiplied by 100 to give a percentage (e.g. 95% accurate).
- We will be using the metric variable when we run build and evaluate each model next.

Build 5 different models to predict species from flower measurements

Linear Discriminant Analysis (LDA); simple linear
Classification and Regression Trees (CART); nonlinear
k-Nearest Neighbors (kNN); nonlinear
Support Vector Machines (SVM) with a linear kernel; complex linear
Random Forest (RF); complex linear

Select the best model

Test Harness

control <- trainControl(method = "cv", number = 10)
metric <- "Accuracy"

Build models

# Linear Discriminant Analysis (LDA)
set.seed(7)
fit.lda <- train(Species ~ ., data = dtTest, method = "lda", metric = metric, trControl = control)

# Classification and Regression Trees (CART)
set.seed(7)
fit.cart <- train(Species ~ ., data = dtTest, method = "rpart", metric = metric, trControl = control)

# k-Nearest Neighbors (kNN)
set.seed(7)
fit.knn <- train(Species ~ ., data = dtTest, method = "knn", metric = metric, trControl = control)

# Support Vector Machines (SVM) with a linear kernel
set.seed(7)
fit.svm <- train(Species ~ ., data = dtTest, method = "svmRadial", metric = metric, trControl = control)

# Random Forest (RF)
set.seed(7)
fit.rf <- train(Species ~ ., data = dtTest, method = "rf", metric = metric, trControl = control)

Select the best model

# sumarize accuracy of the models
results <- resamples(list(lda = fit.lda, cart = fit.cart, knn = fit.knn, svm = fit.svm, rf = fit.rf))
summary(results)

## 
## Call:
## summary.resamples(object = results)
## 
## Models: lda, cart, knn, svm, rf 
## Number of resamples: 10 
## 
## Accuracy 
##           Min.   1st Qu.    Median      Mean   3rd Qu. Max. NA's
## lda  0.9166667 0.9375000 1.0000000 0.9750000 1.0000000    1    0
## cart 0.8333333 0.9166667 0.9166667 0.9250000 0.9791667    1    0
## knn  0.9166667 0.9166667 1.0000000 0.9666667 1.0000000    1    0
## svm  0.9166667 0.9166667 0.9166667 0.9500000 1.0000000    1    0
## rf   0.9166667 0.9166667 0.9166667 0.9500000 1.0000000    1    0
## 
## Kappa 
##       Min. 1st Qu. Median   Mean 3rd Qu. Max. NA's
## lda  0.875 0.90625  1.000 0.9625 1.00000    1    0
## cart 0.750 0.87500  0.875 0.8875 0.96875    1    0
## knn  0.875 0.87500  1.000 0.9500 1.00000    1    0
## svm  0.875 0.87500  0.875 0.9250 1.00000    1    0
## rf   0.875 0.87500  0.875 0.9250 1.00000    1    0

# compare the accuracy of the models
dotplot(results)

# summarize the best model
print(fit.lda)

## Linear Discriminant Analysis 
## 
## 120 samples
##   4 predictor
##   3 classes: 'setosa', 'versicolor', 'virginica' 
## 
## No pre-processing
## Resampling: Cross-Validated (10 fold) 
## Summary of sample sizes: 108, 108, 108, 108, 108, 108, ... 
## Resampling results:
## 
##   Accuracy  Kappa 
##   0.975     0.9625

Make Predictions

since LDA was the most accurate, run LDA on the validation

# estimate skill of LDA
predictions <- predict(fit.lda, dtValidation)
confusionMatrix(predictions, dtValidation$Species)

## Confusion Matrix and Statistics
## 
##             Reference
## Prediction   setosa versicolor virginica
##   setosa         10          0         0
##   versicolor      0         10         0
##   virginica       0          0        10
## 
## Overall Statistics
##                                      
##                Accuracy : 1          
##                  95% CI : (0.8843, 1)
##     No Information Rate : 0.3333     
##     P-Value [Acc > NIR] : 4.857e-15  
##                                      
##                   Kappa : 1          
##                                      
##  Mcnemar's Test P-Value : NA         
## 
## Statistics by Class:
## 
##                      Class: setosa Class: versicolor Class: virginica
## Sensitivity                 1.0000            1.0000           1.0000
## Specificity                 1.0000            1.0000           1.0000
## Pos Pred Value              1.0000            1.0000           1.0000
## Neg Pred Value              1.0000            1.0000           1.0000
## Prevalence                  0.3333            0.3333           0.3333
## Detection Rate              0.3333            0.3333           0.3333
## Detection Prevalence        0.3333            0.3333           0.3333
## Balanced Accuracy           1.0000            1.0000           1.0000